#!/usr/bin/env python3
#
# BSD 3-Clause License
#
# Copyright (c) 2017 xxxx
# All rights reserved.
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
#   contributors may be used to endorse or promote products derived from
#   this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ============================================================================
#
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.

"""Multiprocessing helpers."""

import torch
import torch.npu
import os
NPU_CALCULATE_DEVICE = 0
if os.getenv('NPU_CALCULATE_DEVICE') and str.isdigit(os.getenv('NPU_CALCULATE_DEVICE')):
    NPU_CALCULATE_DEVICE = int(os.getenv('NPU_CALCULATE_DEVICE'))
if torch.npu.current_device() != NPU_CALCULATE_DEVICE:
    torch.npu.set_device(f'npu:{NPU_CALCULATE_DEVICE}')


def run(
    local_rank,
    num_proc,
    func,
    init_method,
    shard_id,
    num_shards,
    backend,
    cfg,
    output_queue=None,
):
    """
    Runs a function from a child process.
    Args:
        local_rank (int): rank of the current process on the current machine.
        num_proc (int): number of processes per machine.
        func (function): function to execute on each of the process.
        init_method (string): method to initialize the distributed training.
            TCP initialization: equiring a network address reachable from all
            processes followed by the port.
            Shared file-system initialization: makes use of a file system that
            is shared and visible from all machines. The URL should start with
            file:// and contain a path to a non-existent file on a shared file
            system.
        shard_id (int): the rank of the current machine.
        num_shards (int): number of overall machines for the distributed
            training job.
        backend (string): three distributed backends ('nccl', 'gloo', 'mpi') are
            supports, each with different capabilities. Details can be found
            here:
            https://pytorch.org/docs/stable/distributed.html
        cfg (CfgNode): configs. Details can be found in
            slowfast/config/defaults.py
        output_queue (queue): can optionally be used to return values from the
            master process.
    """
    # Initialize the process group.
    world_size = num_proc * num_shards
    rank = shard_id * num_proc + local_rank

    try:
        torch.distributed.init_process_group(
            'hccl', init_method=init_method,
            world_size=world_size,
            rank=rank,
        )
    except Exception as e:
        raise e

    torch.npu.set_device(local_rank)
    ret = func(cfg)
    if output_queue is not None and local_rank == 0:
        output_queue.put(ret)
